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The book provides an overview of what it means to create personality-like, thinking, reasoning, deciding technologies. It reviews the development of cognitive modeling as theory based on the principles of psychology and neuroscience and as used in Artificial Intelligence (AI), Machine Learning (ML), and human computer interaction. The chapters deal with topics like cognitive architectures, knowledge representation, natural language processing, and adaptive learning algorithms. It accentuates how digital personalities complement and amplify real-world application solutions that include a virtual assistant, social robot, autonomous systems, and decision-support systems. Includes several case studies and examples from actual lives wherein the authors demonstrate how prominent cognitive models become the leading ideas for developing health cares, educational projects and smart surroundings. It also discusses useful and interesting ethical and philosophical issues that relate to identity, existence of consciousness, and humans' interaction with AI. It examines the effect of having digital agents that imitate and emulate humans, as well as to some extent, control them.
Adaptive learning is treated as a layered process: Reinforcement Learning algorithms coupled with predictive analytics, trait-conditioned policy shaping, and mathematically modeled emotional appraisal that influences operator prioritization and credit assignment. Emotional modeling and its adaptive regulation are presented not as embellishments but as control-theoretic regulators mediating stability–plasticity trade-offs. Behavioral simulation chapters extend these mechanisms into goal-driven, pattern-recognizing, and emotion-influenced action selection, formalized through numerical and statistical modeling pipelines to enable reproducible evaluation.
This book is intended for researchers and practitioners in AI, cognitive modeling, affective computing, HCI, autonomous systems, and applied simulation. This book provides both a conceptual scaffold and an implementation-oriented methodology. By aligning cognitive architecture rigor with operational realism, it aspires to move digital personality design from ad hoc persona scripting toward principled, quantitatively grounded, and evolution-ready cognitive engineering.
Preface
Introduction to Soar and Digital Personalities
Computational Methods in Digital Personality Modeling
Human-Like Reasoning and Cognitive Modeling
Decision-Making Algorithms in Soar
Memory and Learning Mechanisms
Adaptive Learning in Soar-Based Systems
Cognitive and Behavioural Simulations
Real-Time Cognitive Processing in Soar
Personality Profiling, Recognition and Reasoning
Advanced Problem-Solving and Applications
Case Studies of Soar in Digital Personalities
Future Directions and Challenges in Soar-Based Digital Personalities
